1. Field of the Invention
The present invention relates to a system and method for analyzing motions and, more particularly, to a system and method for analyzing the motions of an object based on the silhouettes of the object in video streams that are collected in real time.
This work was supported by the IT R&D program of MIC/IITA[2006-S-026-01, Development of the URC Server Framework for Proactive Robotic Services]
2. Description of the Related Art
In a computer vision field, various researches have been in progress for classifying motion patterns of human from video streams provided from a camera. Since the technologies of the computer vision field require complicated mathematic background and high-technology hardware, it is very difficult to apply the technologies related to the computer vision field into the real life. Especially, the most of technologies related to human motion recognition, including systems and methods, require mass amount of computation.
The technologies of human motion recognition are classified into face recognition, gesture recognition, and motion recognition. The human motion recognition includes three procedures, a preprocessing step, an analysis step, and a recognition step. Since various processes are performed at each of the steps, the human motion recognition may be considered as one huge system.
At the preprocess step, a background is separated from an image. Various methods for separating the background from an image were introduced. These methods have complementary performance characteristics in views of a processing speed and accuracy. For example, if one of the background separation methods has a fast processing speed, it has less accuracy and vice versa.
At the analysis step, meaningful features are extracted to extract the motion patterns of human to analyze. Various feature extracting methods have been researched. In general, a method of extracting morphological features of objects or a method for extracting features by estimating the shape of an object through a probabilistic model has been used. However, these methods have a problem of generating noise while extracting features or a problem of extracting wrong features.
At the recognition step, a statistical probability based recognition method or a learning based recognition method such as a neural network has been widely used. These methods have been actively researched in an artificial intelligent field and a machine learning field. However, these methods require the mass amount of complicated calculation. Therefore, it is difficult to process the recognition step in real time using these methods.